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A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm

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  • Hu, Jinxing
  • Li, Hongru

Abstract

In the stochastic optimal scheduling of microgrid with multiple wind farms, the accurate description of uncertainties is a critical issue. Scenario generation provides an effective way to represent the strong randomness and interdependence between wind speeds. However, there may be very limited data or no historical information in the beginning stage of a newly-built wind farm operation, which will lead to the inaccuracy of scenario generation and thus affect the reliability of decision results. In this paper, considering that multiple wind farms in the adjacent areas may have similar weather conditions, a novel transfer learning-based scenario generation method is proposed to utilize the historical information from other existing data-rich farms for generating wind speed scenarios of the new farm. The scenario generation tasks are constructed as a cross-domain adaption problem. To model the target wind speed, joint distribution adaption (JDA) is adopted to explore the underlying relationship between multiple source farms and the target farm. Experimental results show that the scenarios generated by our proposed method can better describe the properties of target wind speed, and the microgrid scheduling results can be more reliable in the case of very limited data.

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  • Hu, Jinxing & Li, Hongru, 2022. "A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm," Renewable Energy, Elsevier, vol. 185(C), pages 1139-1151.
    • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:1139-1151
    DOI: 10.1016/j.renene.2021.12.110
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    Cited by:

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    2. Jin, Huaiping & Yang, Guanzhi & Dong, Shoulong & Fan, Shouyuan & Jin, Huaikang & Wang, Bin, 2025. "Wind power forecasting for newly built wind farms based on deep learning with dual-stage attention mechanism and adaptive transfer learning," Energy, Elsevier, vol. 335(C).
    3. Hu, Jinxing & Cao, Yimai & Tan, Guoqiang, 2025. "A dynamic spatiotemporal graph generative adversarial network for scenario generation of renewable energy with nonlinear dependence," Energy, Elsevier, vol. 335(C).
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    5. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Huang, Yuehui & Chen, Jianjun & Li, Yahe & Chen, Zhe & Blaabjerg, Frede, 2024. "Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms," Applied Energy, Elsevier, vol. 355(C).
    6. Hu, Jinxing & Yan, Pengqian & Tan, Guoqiang, 2025. "Spatiotemporal dependence modeling of wind speeds via adaptive-selected mixture pair copulas for scenario-based applications," Renewable Energy, Elsevier, vol. 244(C).
    7. Fei Feng & Xin Du & Qiang Si & Hao Cai, 2022. "Hybrid Game Optimization of Microgrid Cluster (MC) Based on Service Provider (SP) and Tiered Carbon Price," Energies, MDPI, vol. 15(14), pages 1-22, July.
    8. Dong, Xiaochong & Sun, Yingyun & Dong, Lei & Li, Jian & Li, Yan & Di, Lei, 2023. "Transferable wind power probabilistic forecasting based on multi-domain adversarial networks," Energy, Elsevier, vol. 285(C).
    9. Xin Ren & Yimei Wang & Zhi Cao & Fuhao Chen & Yujia Li & Jie Yan, 2023. "Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting," Energies, MDPI, vol. 16(17), pages 1-13, August.
    10. Xinghua Wang & Xixian Liu & Fucheng Zhong & Zilv Li & Kaiguo Xuan & Zhuoli Zhao, 2023. "A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    11. Jian Tang & Jianfei Liu & Jinghan Wu & Guofeng Jin & Heran Kang & Zhao Zhang & Nantian Huang, 2023. "RAC-GAN-Based Scenario Generation for Newly Built Wind Farm," Energies, MDPI, vol. 16(5), pages 1-17, March.
    12. Han, Shuo & Yuan, Yifan & He, Mengjiao & Zhao, Ziwen & Xu, Beibei & Chen, Diyi & Jurasz, Jakub, 2024. "A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system," Applied Energy, Elsevier, vol. 356(C).
    13. Feng, Zhong-kai & Wang, Xin & Niu, Wen-jing, 2025. "Complementary operation optimization of cascade hydropower reservoirs and photovoltaic energy using cooperation search algorithm and conditional generative adversarial networks," Energy, Elsevier, vol. 328(C).
    14. Gao, Fang & Xu, Zidong & Yin, Linfei, 2024. "Bayesian deep neural networks for spatio-temporal probabilistic optimal power flow with multi-source renewable energy," Applied Energy, Elsevier, vol. 353(PA).
    15. Ouyang, Tiancheng & Zhang, Mingliang & Qin, Peijia & Tan, Xianlin, 2024. "Flow battery energy storage system for microgrid peak shaving based on predictive control algorithm," Applied Energy, Elsevier, vol. 356(C).
    16. Xiaomei Ma & Yongqian Liu & Jie Yan & Han Wang, 2023. "A WGAN-GP-Based Scenarios Generation Method for Wind and Solar Power Complementary Study," Energies, MDPI, vol. 16(7), pages 1-20, March.

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